SCISIPBIO: Maximizing the Value of Sex-Inclusive Research Policies through Analytical Rigor

SCISIPBIO：通过严谨的分析最大限度地发挥性别包容性研究政策的价值

• 批准号: 2318478
• 负责人: Andrew Brown
• 金额: $ 26.44万
• 依托单位: Arkansas Childrens Hospital Research Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318478&HistoricalAwards=false
• 关键词: SCISIPBIO; Maximizing; Value; Sex; Inclusive

Historically, preclinical research has focused disproportionately on males. This bias has limited the progress of science to advance national health and welfare. In 2016, to encourage inclusion of both males and females in research, the National Institutes of Health implemented a policy requiring the consideration of sex as a biological variable (SABV) in all funded studies. Although inclusion of females in research has improved, sex-based data are often not being analyzed using rigorous approaches. Our preliminary analysis of SABV-compliant studies showed that when authors report sex-specific effects, they tested statistically for such effects only 29% of the time. Instead, claims of sex-specific findings typically rest on assertion alone. Our previous findings indicate clearly that the implementation of SABV has not been rigorous and that the process of peer review of SABV-compliant research needs to improve. False claims of sex differences can lead to the wasting of resources on ‘differences’ that do not exist, and to inequities in access to effective treatments. Similarly, false claims of non-differences can lead to missed opportunities to provide effective health care for people of all sexes and genders. The intended goals of SABV, namely to enhance reproducibility and to facilitate sex-based precision medicine, are not being met. In this project we will determine the impact of inappropriate analytical approaches on the accurate reporting of sex-specific effects, identify factors that inform decisions about analytical approaches to sex-based data, and disseminate tools for designing and evaluating studies that consider SABV. We will use a mixed method approach that includes analyses of published journal articles, interviews with scientists, and outreach. We will conduct a large-scale analysis of the biomedical literature to show how inappropriate statistical approaches to sex-based data are impacting the rigorous implementation of SABV. Second, we will conduct semi-structured interviews with authors of some of those publications to identify the factors that facilitate best practices. Together, these methods will inform the development of a workflow for appropriate analysis of sex-based data. Third, we will make our optimized workflow publicly available and disseminate it widely. In addition, we will partner with journal editors to develop a simple tool that can be used by reviewers to evaluate the validity of sex-specific findings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

从历史上看，临床前研究不成比例地关注男性。这种偏见限制了促进国民健康和福利的科学进步。 2016 年，为了鼓励男性和女性参与研究，美国国立卫生研究院实施了一项政策，要求在所有资助的研究中将性别视为生物变量 (SABV)。尽管女性参与研究的情况有所改善，但基于性别的数据往往没有使用严格的方法进行分析。我们对 SABV 合规研究的初步分析表明，当作者报告性别特异性影响时，他们仅在 29% 的情况下对此类影响进行了统计测试。相反，对特定性别发现的主张通常仅基于断言。我们之前的研究结果清楚地表明，SABV 的实施并不严格，符合 SABV 的研究的同行评审过程需要改进。对性别差异的错误说法可能会导致资源浪费在不存在的“差异”上，并导致获得有效治疗的不平等。同样，虚假的无差异声明可能会导致错失为所有性别和性别的人提供有效医疗保健的机会。 SABV 的预期目标，即提高可重复性和促进基于性别的精准医疗，尚未实现。在这个项目中，我们将确定不适当的分析方法对准确报告特定性别影响的影响，确定为基于性别的数据分析方法决策提供信息的因素，并传播用于设计和评估考虑 SABV 的研究的工具。我们将使用混合方法，包括对已发表期刊文章的分析、对科学家的采访以及外展活动。我们将对生物医学文献进行大规模分析，以展示基于性别的数据的不适当统计方法如何影响 SABV 的严格实施。其次，我们将对其中一些出版物的作者进行半结构化访谈，以确定促进最佳实践的因素。这些方法共同将为基于性别的数据进行适当分析的工作流程的开发提供信息。第三，我们将把优化后的工作流程公开并广泛传播。此外，我们将与期刊编辑合作开发一个简单的工具，可供审稿人用来评估特定性别研究结果的有效性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响评审标准进行评估，被认为值得支持。